AI RESEARCH
From Explanations to Architecture: Explainability-Driven CNN Refinement for Brain Tumor Classification in MRI
arXiv CS.CV
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ArXi:2506.09161v3 Announce Type: replace-cross Recent brain tumor classification methods often report high accuracy but rely on deep, over-parameterized architectures with limited interpretability, making it difficult to determine whether predictions are driven by tumor-relevant evidence or by spurious cues such as background artifacts or normal tissue. We propose an explainable convolutional neural network (CNN) framework that enhances model transparency without sacrificing classification accuracy.